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Support vector machines for recognition of semi-arid vegetation types using MISR multi-angle imagery by Lihong Su; Mark J. Chopping; Albert Rango; John V. Martonchik; Debra P.C. Peters is a Environmental Science article available to read on EtoBox.

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Accurately mapping community types is one of the main challenges for monitoring arid and semi-arid grasslands with remote sensing. The multi-angle approach has been proven useful for mapping vegetation types in desert grassland. The Multi-angle Imaging Spectro-Radiometer (MISR) provides 4 spectral bands and 9 angular reflectance. In this study, 44 classification experiments have been implemented to find the optimal combination of MISR multi-angular data to mine the information carried by MISR data as effectively as possible. These experiments show the following findings: 1) The combination of MISR's 4 spectral bands at nadir and red and near infrared bands in the C, B, and A cameras observing off-nadir can obtain the best vegetation type differentiation at the community level in New Mexico desert grasslands. 2) The k parameter at red band of Modified-Rahman-Pinty-Verstraete (MRPV) model and the structural scattering index (SSI) can bring useful additional information to land cover classification. The information carried by these two parameters, however, is less than that carried by surface anisotropy patterns described by the MRPV model and a linear semi-empirical kernel-driven bid

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Author

Lihong Su; Mark J. Chopping; Albert Rango; John V. Martonchik; Debra P.C. Peters

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0034-4257)

Published

2007

Language

EN

Field

Environmental Science (Physical Sciences)